Securing Social Network Data from Privacy Threats

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Introduction

Data management is an outgoing concern in the contemporary world, and companies use technology for communication. The transfer of information from one person to another is made effective through numerous research. Today, technology has made it possible for people to find the location of each other as they chart. Although technological advancement makes communication easy and efficient, it introduces new challenges of cybercrimes and other information management offenses. When, for example, banks want to make work easier by having online transactions, cybercriminals also advance their research to divert online transactions for personal gain. On the other hand, countries in war may use the military’s mobile devices to locate their enemies and attack them. Technology can therefore be a threat whenever countries are at war or between people communicating and doing business. Technological advancements must therefore be annexed with principles, rules, and regulations to ensure that the information does not fall into the wrong hands. Investment in artificial intelligence and training the personnel in the organization is important in developing positive work culture and improving data security in an organization. An investment must follow every investment in technology in information security to ensure that company, personal, and country’s specific data is safe at all times. This paper covers a review and analysis of literature from 3 different articles.

Literature Review

The first part of the literature review shows the similarities and differences between the three articles: MILC: A secure and privacy-preserving mobile instant locator with chatting and Information Systems Frontiers Loukas et al. (2012), Anonymising group data sharing in opportunistic mobile social networks. Wireless Networks, Adu-Gyamfi, et al. (2021), Secure Military Social Networking and Rapid Sensemaking in Domain Specific Concept Systems: Research Issues and Future Solutions Garside et al. (2012). The discussion shall be based on the articles’ methodology, findings, and recommendations. The second section of the literature analyses the findings from the three reviewed articles.

1. Similarities
   1. Methodology

Similar to the article by Loukas et al. (2012), article Adu-Gyamfi et al. (2021) collected their data from the internet for their case study, and the main purpose of the study was to deal with the issue of data security and privacy the same as it was for article Garside et al. (2012). Article Garside et al. (2012) collected data from social media such as Facebook.

1. Findings

Researchers of articles Loukas et al. (2012) and 2 found that vital information was being shared either through mobile applications or Bluetooth, like in the article by Garside et al. (2012). The conclusions of the articles by Loukas et al. (2012) aresimilar to those of the article Adu-Gyamfi et al. (2021). The number of Bluetooth-enabled smartphones and mobile applications is expected to grow significantly over the next few years, and they both discovered an authentication scheme for the privacy protection of group users while sharing data in the form of packets. However, the majority of these applications have failed to provide their users with truly secure and private services. At the same time, results from the article by Garside et al. (2012) also suggested that utilizing social networking sites might put military personnel’s family and friends under some stress, which might, in some situations, outweigh the major advantages of social networking for those left behind when active duty soldiers are deployed. All three articles showed how it was so crucial to protect data shared on any platform. It was similar in all articles that whether personal or group information was not supposed to be unprotected.

1. Recommendations

Articles Loukas et al. (2012) and Garside et al. (2012) recommended better approaches that can be used to curb the insecurities which are associated with a lack of security and privacy by sharing information. Article Loukas et al. (2012) discussed the use of mobile applications similar to article Loukas et al. (2012) and Garside et al. (2012), while article Adu-Gyamfi et al. (2021) suggested the same way.

2. Differences
   1. Methodology

Unlike the article by Loukas et al. (2012), which applied quantitative research methodology in the collection of data, the article Adu-Gyamfi et al. (2021) used a literature survey in collecting data, while the article by Garside et al. (2012) collected its data by using personal security and operational security policy documents. Articles Loukas et al. (2012) collected data from the internet, while researchers of article Adu-Gyamfi et al. (2021) collected data from both the internet and business media. Contrary to the articles by Loukas et al. (2012) and Adu-Gyamfi et al. (2021), article 3 sourced its data from Facebook and other social media applications.

1. Findings

            In contrast, the researchers in the article by Loukas et al. (2012) found that in the future, there is a high possibility of an increase in mobile applications, and most of these applications were not secure and did not have privacy services while the article Adu-Gyamfi et al. (2021) found an authentication system that could offer privacy to group users in case they need to share information through Bluetooth in smartphones thus increasing the reliability of the scheme and reduction of disclosure of group members identity. On contrary to the articles by Loukas et al. (2012) and Adu-Gyamfi et al. (2021) findings, in the article by Garside et al. (2012), there were concerns about the risks which are related to social networking by military families and their friends. There was a lot of fear caused by the unintentional disclosure of information.

1. Recommendations

Article Loukas et al. (2012) recommended using MICL to provide security and privacy to its users through both asymmetric and symmetric cryptography. MICL is a system that incorporates private communities such as research groups and student communities with the main aim of providing education. While on the other hand, the article Adu-Gyamfi et al. (2021) suggested using a cryptographic encryption technique to shield users’ data privacy and identity. Unlike articles Loukas et al. (2012) and Adu-Gyamfi et al. (2021), article three did not address the issue of data security and privacy, but it suggested that a lot of the information on social networking sites can be used to support quick sense making.

Part 2: Literature Analysis

Loukas,Athanasiosetal.(2012)discusses abouttheMILCapplication that provides location-based services and mobile instant messaging by providing anacceptable level of security by using cryptography. A literature review was the methodology usedto collect data from internet sources and business media. The researchers have found that mobileapplicationsareexpectedtoincreaserapidlyoverthenextfewyears, andmostoftheapplicationshavefailedtodelivertrulysecureandprivacyservicestotheirusers.MILCintegrateswithprivatecommunities mainly for education reasons like student communities, research groups, etc. MILCuses both symmetric and asymmetric cryptography to provide a high level of security to its usersandrespectsend-userprivacybyputtingtheuserincontrolofwhatprivateinformationisrevealedtootherparties.

Similarly, Adu-Gyamfi, Daniel, et al. (2021) conducted research between December 2020 andJanuary 2021 to learn about the compilation of cryptographic encryption protocol schemes toprotect the identity and data privacy of the users for a large-scale opportunistic mobile socialnetworking(OMSN).Thequantitativeresearchmethodologywasusedtocollectthe data from internet sources. The researchers have found an authentication scheme for theprivacyprotectionofgroupuserswhilesharingdataintheformofpacketsviaBluetooth-enabledsmartphones. This scheme achieves high reliability of packet notification forwarding for groupusersbyreducingtheidentitydisclosureofgroupmembersduringOSMNtoprotecttheprivacyofalargegroupofusers.

Also, Debbie Garside, Arjun Ponnusamy, Steve Chan, and Richard Picking conducted the research in 2011 by gathering information from Facebook posts and other social media accounts of friends and family members of military personnel in order to examine the demand for a secure military social network as well as the fundamental problems that must be solved before such networks can be successfully constructed.The study argues that social networking sites can provide vital information for rapid governance and rapid decision-making processes during abrupt governance and eco-system transition. The study also discusses ways to analyze and use the vast amount of information on social networking sites to promote rapid sensemaking.The fear of mistakenly revealing information seems to be pervasive. Using social networking sites might put military personnel’s family and friends under stress, which might, in some situations, outweigh the major advantages of social networking for those left behind when active duty soldiers are deployed.

Discussion

Technological advancements are increasing people’s connectedness and allowing people to work with one another. However, the data management system’s improvement jeopardizes security and threatens people’s privacy. For example, military personnel have been targeted with their personnel. Further, corporate espionage is common in the current domain as business information may land in the hands of the wrong people, which may lower a company’s competitive advantages (Adu-Gyamfi et al., 2021). It is imperative to note that humans are the weakest links in technology, and privacy settings must be enhanced to ensure that people are safe at all times. With the increase in mobile applications, terrorists may use the platforms to determine their target’s location and attack their exact location. The ever-increasing scale of data at organizational and personal levels makes data control challenging and increases the risk to humans. Sometimes, there is bad data culture where information is not filtered before sharing, jeopardizing relationships and lowering security levels (Loukas et al., 2012). Incorporating a mobile application with an instant locater and chart helps people locate each other but increases their risk of exposing their personal information and location, which may be the prerequisite for an attack.

Mitigations

The numerous privacy threats introduced by the escalating technological use have been mitigated by the introduction of positive data culture where people in organizations are trained on the best ways of managing data. As people are trained to manage their privacy and location security enhances, the challenge of jeopardized privacy is eradicated, and people can retain their personal information. Most organizations and social media sites have rules and regulations to have people work towards safer and more secure social media. In Facebook, for example, no company can extract people’s personal information without their consent (Adu-Gyamfi et al., 2021). Access control is one of the most common mitigation strategies used by other organizations, such as the military, toensure people access information based on their qualifications. In the military, for example, it is not allowed to access the personal information of people working on a mission to ensure that their privacy is always maintained. The maintenance cost required for technological communication platforms has been increased in many organizations to ensure that people are always safe and their information is protected. Caching private information always makes people safe when using social media platforms.

Recommendations

Data encryption is an important technique that enhances privacy for everyone sharing data over the internet. For example, sending a message is transformed into an unreadable format using a special key, and the receiver is given access to it. Upon receipt of the data, the key is used to convert the message into a readable form, and the message is passed. All third parties are denied access because they do not have the key. It is imperative to note that artificial intelligence and machine learning are key to granting privacy to the people working with data and information. All mobile applications that show location when charting must be embedded with configuration settings where they will be configured to a secure mode, and the user will have the opportunity to allow the people they want to have access to their location so that strangers are not able to access the personal private information (Garside et al., 2012). In the military and other business organizations, constant training on security features must be conducted to make people aware of data privacy issues they are supposed to know. Continuous training on data privacy is important to ensure that all people working in the organization know the security threats and protect the organization.

Conclusion

In a nutshell, the change in technology and development in the information technology domain has negative impacts on privacy and data management. The rate of cybercrimes has increased as more people leverage technology for their communication. It is therefore the role of organizational management to invest in data protection and artificial intelligence to protect people and their information. More laws and regulations must be put in place whenever using technology and mobile applications must be cascaded with configuration tools for data protection. Businesses, individuals, and nations are all at risk when using mobile applications and must therefore understand the risk of the technology before using it.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

References

Adu-Gyamfi, D., Zhang, F., &Takyi, A. (2021). Anonymizing group data sharing in opportunistic mobile social networks. Wireless Networks, 27(2), 1477-1490.https://link.springer.com/article/10.1007/s11276-020-02524-8

Garside, D., Ponnusamy, A., Chan, S., & Picking, R. (2012). Secure military social networking and rapid sensemaking in domain-specific concept systems: research issues and future solutions. Future Internet, 4(1), 253–264.https://doi.org/10.3390/fi4010253

Loukas, A., Damopoulos, D., Menesidou, S. A., Skarkala, M. E., Kambourakis, G., &Gritzalis, S. (2012). MILC: A secure and privacy-preserving mobile instant locator with chatting. Information Systems Frontiers, 14(3), 481-497.https://link.springer.com/article/10.1007/s10796-010-9254-0